Managing data transformation tools using generative artificial intelligence

The disclosure relates generally to an improved computer system and more specifically to managing data transformation tools for data manipulation.

Data transformation tools are software products that are designed to facilitate the process of converting data from one format, structure, or representation to another. Data transformation tools can be used to manipulate, refine, and enrich data to meet the requirements needed for a specific software application. Therefore, data transformation tools play important roles in data manipulation, data integration, and data extraction processes.

Data transformation tools usually provide a graphical user interface and/or scripting capability for defining mapping and transformation logic of data across different software applications. In this case, the transformation logic indicates how data from diverse sources should be manipulated to meet the desired format or structure of other software applications.

With the increased adoption of data transformation tools, organizations can extract maximum value from data and exchange data freely without the concern of external data being incompatible. As a result, the use of data transformation tools enables organizations to optimize data manipulation and refinement across different software applications.

According to one illustrative embodiment, a computer implemented method manages data transformation tools. A processor set selects a foundation model to process respective user manuals for a number of data transformation tools. The processor set generates a large language model from the foundation model based on user manuals for the number of data transformation tools and a number of scripts for automation bots for the number of data transformation tools. The processor set generates a number of new automation bots for each data transformation tool in the number of data transformation tools using the large language model. The processor set performs a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automaton bots. According to other illustrative embodiments, a computer system, and a computer program product for managing data transformation tools are provided.

A computer implemented method manages data transformation tools. A processor set selects a foundation model to process respective user manuals for a number of data transformation tools. The number of data transformation tools use different data formats to store information. The processor set generates a large language model from the foundation model based on user manuals for the number of data transformation tools and a number of scripts for automation bots for the number of data transformation tools. The large language model is an artificial intelligence model that performs natural language processing to generate instructions for automation bots based on the user manuals and the number of scripts for the automation bots. The processor set generates a number of new automation bots for each data transformation tool in the number of data transformation tools using the large language model. Each new automation bot is configured to perform an operation in data transformation tools associated with each new automation bot. The processor set performs a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots. As a result, the illustrative embodiments provide a technical effect of generating automation bots based on a specifically trained large language model and automate the workflow for data transformation tools.

In the illustrative embodiments, as part of performing a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots, the processor set selects a first data transformation tool and a second data transformation tool from the number of data transformation tools. The processor set retrieves a first data transformation map from the first data transformation tool using a first automation bot for the first data transformation tool. The first data transformation map is a pre-existing data transformation map, and the first automation bot is an automation bot from the number of new automation bots. The processor set creates a first mapping specification based on the first data transformation map using the first automation bot for the first data transformation tool. The processor set retrieves the first mapping specification using a second automation bot for the second data transformation tool. The second automation bot is an automation bot from the number of new automation bots. The processor set creates a new data transformation map in the second data transformation tool based on the first mapping specification using the second automation bot for the second data transformation tool. Thus, the illustrative embodiments provide a technical effect of automating migration of data transformation maps from a data transformation tool to another data transformation tool.

In the illustrative embodiments, as part of performing a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots, the processor set selects a data transformation tool from the number of data transformation tools. The processor set retrieves a mapping specification and a data transformation map for the selected data transformation tool using a third automation bot for the selected data transformation tool. The third automation bot is an automation bot from the number of new automation bots. The processor set modifies the data transformation map based on the mapping specification in the selected data transformation tool using the third automation bot for the selected data transformation tool. As a result, the illustrative embodiments provide a technical effect of automating modification of data transformation maps in a data transformation tool.

In the illustrative embodiments, as part of performing a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots, the processor set selects a data transformation tool from the number of data transformation tools. The processor set retrieves a mapping specification for the data transformation tool using a fourth automation bot for the data transformation tool. The fourth automation bot is an automation bot from the number of new automation bots. The processor set creates a new data transformation map in the data transformation tool using the fourth automation bot for the data transformation tool. As a result, the illustrative embodiments provide a technical effect of automating creation of data transformation maps in a data transformation tool.

In the illustrative embodiments, the processor set integrates the number of new automation bots for the number of data transformation tools into an orchestration tool to automate workflows for the number of new automation bots. As a result, the illustrative embodiments provide a technical effect of using an orchestration tool to integrate all new automation bots and provide a platform for automating workflows in different scenarios for all new automation bots.

In the illustrative embodiments, the number of operations in the number of data transformation tools are performed using visual data transformation map designers of respective data transformation tools or through HTML export of transformation maps. Thus, the illustrative embodiments provide a technical effect of using automation bots to perform operations in a specially designed user interface or through HTML export of data transformation maps for each data transformation tool.

In the illustrative embodiments, the foundation model is selected based on language used in the user manuals for the number of data transformation tools. Thus, the illustrative embodiments provide a technical effect of training the foundation model to generate a large language model that is specific to the language used for particular scenarios.

A computer system comprises a processor set, a set of one or more computer-readable storage media, and program instructions, collectively stored in the set of one or more storage media, for causing the processor set to perform the following computer operations. The processor set selects a foundation model to process respective user manuals for a number of data transformation tools. The number of data transformation tools use different data formats to store information. The processor set generates a large language model from the foundation model based on user manuals for the number of data transformation tools and a number of scripts for automation bots for the number of data transformation tools. The large language model is an artificial intelligence model that performs natural language processing to generate instructions for automation bots based on the user manuals and the number of scripts for the automation bots. The processor set generates a number of new automation bots for each data transformation tool in the number of data transformation tools using the large language model. Each automation bot is configured to perform an operation in data transformation tools associated with each new automation bot. The processor set performs a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots. As a result, the illustrative embodiments provide a technical effect of generating automation bots based on a specifically trained large language model and automate the workflow for data transformation tools.

In the illustrative embodiments, as part of performing a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots, the processor set further executes the program instructions to select a first data transformation tool and a second data transformation tool from the number of data transformation tools. The processor set further executes the program instructions to retrieve a first data transformation map from the first data transformation tool using a first automation bot for the first data transformation tool. The first data transformation map is a pre-existing data transformation map, and the first automation bot is an automation bot from the number of new automation bots. The processor set further executes the program instructions to create a first mapping specification based on the first data transformation map using the first automation bot for the first data transformation tool. The processor set further executes the program instructions to retrieve the first mapping specification using a second automation bot for the second data transformation tool. The second automation bot is an automation bot from the number of new automation bots. The processor set further executes the program instructions to create a new data transformation map in the second data transformation tool based on the first mapping specification using the second automation bot for the second data transformation tool. Thus, the illustrative embodiments provide a technical effect of automating migration of data transformation maps from a data transformation tool to another data transformation tool.

In the illustrative embodiments, as part of performing a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots, the processor set further executes the program instructions to select a data transformation tool from the number of data transformation tools. The processor set further executes the program instructions to retrieve a mapping specification and a data transformation map for the selected data transformation tool using a third automation bot for the selected data transformation tool. The third automation bot is an automation bot from the number of new automation bots. The processor set further executes the program instructions to modify the data transformation map based on the mapping specification in the selected data transformation tool using the third automation bot for the selected data transformation tool. As a result, the illustrative embodiments provide a technical effect of automating modification of data transformation maps in a data transformation tool.

In the illustrative embodiments, as part of performing a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots, the processor set further executes the program instructions to select a data transformation tool from the number of data transformation tools. The processor set further executes the program instructions to retrieve a mapping specification for the data transformation tool using a fourth automation bot for the data transformation tool. The fourth automation bot is an automation bot from the number of new automation bots. The processor set further executes the program instructions to create a new data transformation map in the data transformation tool using the fourth automation bot for the data transformation tool. As a result, the illustrative embodiments provide a technical effect of automating the creation of a data transformation map in a data transformation tool.

In the illustrative embodiments, the processor set further executes the program instructions to integrate the number of new automation bots for the number of data transformation tools into an orchestration tool to automate workflows for the number of new automation bots. As a result, the illustrative embodiments provide a technical effect of using an orchestration tool to integrate all new automation bots and provide a platform for automating workflows in different scenarios for all new automation bots.

In the illustrative embodiments, the number of operations in the number of data transformation tools are performed using visual data transformation map designers of respective data transformation tools or through HTML export of data transformation maps. Thus, the illustrative embodiments provide a technical effect of using automation bots to perform operations in a specially designed user interface for each data transformation tool.

In the illustrative embodiments, the foundation model is selected based on language used in the user manuals for the number of data transformation tools. Thus, the illustrative embodiments provide a technical effect of training the foundation model to generate a large language model that is specific to the language used for particular scenarios.

In the illustrative embodiments, a computer program product manages data transformation tools. The computer program product comprises a set of one or more computer-readable storage media and program instructions, collectively stored in the set of one or more storage media, for causing a processor set to perform the following computer operations. The program instructions are executable by a computer system to select a foundation model to process respective user manuals for a number of data transformation tools. The number of data transformation tools use different data formats to store information. The program instructions are executable by a computer system to cause the computer system to generate a large language model from the foundation model based on user manuals for the number of data transformation tools and a number of scripts for automation bots for the number of data transformation tools. The large language model is an artificial intelligence model that performs natural language processing to generate instructions for automation bots based on the user manuals and the number of scripts for the automation bots. The program instructions are executable by a computer system to cause the computer system to generate a number of new automation bots for each data transformation tool in the number of data transformation tools using the large language model. Each new automation bot is configured to perform an operation in data transformation tools associated with each new automation bot. The program instructions are executable by a computer system to cause the computer system to perform a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots. As a result, the illustrative embodiments provide a technical effect of generating automation bots based on a specifically trained large language model and automate the workflow for data transformation tools.

In the illustrative embodiments, as part of performing a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots, the program instructions are further executable by the computer system to cause the computer system to select a first data transformation tool and a second data transformation tool from the number of data transformation tools. The program instructions are further executable by the computer system to cause the computer system to retrieve a first data transformation map from the first data transformation tool using a first automation bot for the first data transformation tool. The first data transformation map is a pre-existing data transformation map, and the first automation bot is an automation bot from the number of new automation bots. The program instructions are further executable by the computer system to cause the computer system to create a first mapping specification based on the first data transformation map using the first automation bot for the first data transformation tool. The program instructions are further executable by the computer system to cause the computer system to retrieve the first mapping specification using a second automation bot for the second data transformation tool. The second automation bot is an automation bot from the number of new automation bots. The program instructions are further executable by the computer system to cause the computer system to create a new data transformation map in the second data transformation tool based on the first mapping specification using the second automation bot for the second data transformation tool. Thus, the illustrative embodiments provide a technical effect of automating migration of data transformation maps from a data transformation tool to another data transformation tool.

In the illustrative embodiments, as part of performing a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots, the program instructions are further executable by the computer system to cause the computer system to select a data transformation tool from the number of data transformation tools. The program instructions are further executable by the computer system to cause the computer system to retrieve a mapping specification and a data transformation map for the selected data transformation tool using a third automation bot for the selected data transformation tool. The third automation bot is an automation bot from the number of new automation bots. The program instructions are further executable by the computer system to cause the computer system to modify the data transformation map based on the mapping specification in the selected data transformation tool using the third automation bot for the selected data transformation tool. As a result, the illustrative embodiments provide a technical effect of automating the modification of data transformation maps in a data transformation tool.

In the illustrative embodiments, as part of performing a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots, the program instructions are further executable by the computer system to cause the computer system to select a data transformation tool from the number of data transformation tools. The program instructions are further executable by the computer system to cause the computer system to retrieve a mapping specification for the data transformation tool using a fourth automation bot for the data transformation tool. The fourth automation bot is an automation bot from the number of new automation bots. The program instructions are further executable by the computer system to cause the computer system to create a new data transformation map in the data transformation tool using the fourth automation bot for the data transformation tool. As a result, the illustrative embodiments provide a technical effect of automating the creation of data transformation maps in a data transformation tool.

In the illustrative embodiments, the program instructions are further executable by the computer system to cause the computer system to integrate the number of new automation bots for the number of data transformation tools into an orchestration tool to automate workflows for the number of new automation bots. As a result, the illustrative embodiments provide a technical effect of using orchestration tools to integrate all new automation bots and provide a platform for automating workflows in different scenarios for all new automation bots.

In the illustrative embodiments, the number of operations in the number of data transformation tools are performed using visual data transformation map designers of respective data transformation tools or through HTML export of data transformation maps. Thus, the illustrative embodiments provide a technical effect of using automation bots to perform operations in a specially designed user interface for each data transformation tool.

Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.

A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine-readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer-readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer-readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.

With reference now to the figures in particular with reference to, an illustration of a block diagram of a computing environment is depicted in accordance with an illustrative embodiment. Computing environmentcontains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as data transformation assistant. In addition to data transformation assistant, computing environmentincludes, for example, computer, wide area network (WAN), end user device (EUD), remote server, public cloud, and private cloud. In this embodiment, computerincludes processor set(including processing circuitryand cache), communication fabric, volatile memory, persistent storage(including operating systemand data transformation assistant, as identified above), peripheral device set(including user interface (UI) device set, storage, and Internet of Things (IoT) sensor set), and network module. Remote serverincludes remote database. Public cloudincludes gateway, cloud orchestration module, host physical machine set, virtual machine set, and container set.

COMPUTERmay take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment, detailed discussion is focused on a single computer, specifically computer, to keep the presentation as simple as possible. Computermay be located in a cloud, even though it is not shown in a cloud in. On the other hand, computeris not required to be in a cloud except to any extent as may be affirmatively indicated.

PROCESSOR SETincludes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitrymay be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitrymay implement multiple processor threads and/or multiple processor cores. Cacheis memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor setmay be designed for working with qubits and performing quantum computing.

Computer-readable program instructions are typically loaded onto computerto cause a series of operational steps to be performed by processor setof computerand thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer-readable program instructions are stored in various types of computer-readable storage media, such as cacheand the other storage media discussed below. The program instructions, and associated data, are accessed by processor setto control and direct performance of the inventive methods. In computing environment, at least some of the instructions for performing the inventive methods may be stored in data transformation assistantin persistent storage.

COMMUNICATION FABRICis the signal conduction path that allows the various components of computerto communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up busses, bridges, physical input/output ports and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.

VOLATILE MEMORYis any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, volatile memoryis characterized by random access, but this is not required unless affirmatively indicated. In computer, the volatile memoryis located in a single package and is internal to computer, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer.

PERSISTENT STORAGEis any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computerand/or directly to persistent storage. Persistent storagemay be a read only memory (ROM), but typically at least a portion of the persistent storage allows writing of data, deletion of data and re-writing of data. Some familiar forms of persistent storage include magnetic disks and solid state storage devices. Operating systemmay take several forms, such as various known proprietary operating systems or open source Portable Operating System Interface-type operating systems that employ a kernel. The code included in data transformation assistanttypically includes at least some of the computer code involved in performing the inventive methods.

PERIPHERAL DEVICE SETincludes the set of peripheral devices of computer. Data communication connections between the peripheral devices and the other components of computermay be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion-type connections (for example, secure digital (SD) card), connections made through local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device setmay include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smart watches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storageis external storage, such as an external hard drive, or insertable storage, such as an SD card. Storagemay be persistent and/or volatile. In some embodiments, storagemay take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computeris required to have a large amount of storage (for example, where computerlocally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor setis made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.

NETWORK MODULEis the collection of computer software, hardware, and firmware that allows computerto communicate with other computers through WAN. Network modulemay include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network moduleare performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network moduleare performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer-readable program instructions for performing the inventive methods can typically be downloaded to computerfrom an external computer or external storage device through a network adapter card or network interface included in network module.

WANis any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data, now known or to be developed in the future. In some embodiments, the WANmay be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WAN and/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and edge servers.

END USER DEVICE (EUD)is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer), and may take any of the forms discussed above in connection with computer. EUDtypically receives helpful and useful data from the operations of computer. For example, in a hypothetical case where computeris designed to provide a recommendation to an end user, this recommendation would typically be communicated from network moduleof computerthrough WANto EUD. In this way, EUDcan display, or otherwise present, the recommendation to an end user. In some embodiments, EUDmay be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.

REMOTE SERVERis any computer system that serves at least some data and/or functionality to computer. Remote servermay be controlled and used by the same entity that operates computer. Remote serverrepresents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer. For example, in a hypothetical case where computeris designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computerfrom remote databaseof remote server.

PUBLIC CLOUDis any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloudis performed by the computer hardware and/or software of cloud orchestration module. The computing resources provided by public cloudare typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set, which is the universe of physical computers in and/or available to public cloud. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine setand/or containers from container set. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration modulemanages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gatewayis the collection of computer software, hardware, and firmware that allows public cloudto communicate through WAN.

Some further explanation of virtualized computing environments (VCEs) will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.

PRIVATE CLOUDis similar to public cloud, except that the computing resources are only available for use by a single enterprise. While private cloudis depicted as being in communication with WAN, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloudand private cloudare both part of a larger hybrid cloud.

The illustrative embodiments recognize and take into account a number of different considerations as described herein. For example, the illustrative embodiments recognize and take into account that data transformation tools in different software applications use different proprietary formats to store information. The illustrative embodiments also recognize and take into account that currently there is no method for migrating information of one data transformation tool to another data transformation tool across different software applications in an automated manner.

CLOUD COMPUTING SERVICES AND/OR MICROSERVICES (not separately shown in): private cloudand public cloudare programmed and configured to deliver cloud computing services and/or microservices (unless otherwise indicated, the word “microservices” shall be interpreted as inclusive of larger “services” regardless of size). Cloud services are infrastructure, platforms, or software that are typically hosted by third-party providers and made available to users through the internet. Cloud services facilitate the flow of user data from front-end clients (for example, user-side servers, tablets, desktops, laptops), through the internet, to the provider's systems, and back. In some embodiments, cloud services may be configured and orchestrated according to an “as a service” technology paradigm where something is being presented to an internal or external customer in the form of a cloud computing service. As-a-Service offerings typically provide endpoints with which various customers interface. These endpoints are typically based on a set of APIs. One category of as-a-service offering is Platform as a Service (PaaS), where a service provider provisions, instantiates, runs, and manages a modular bundle of code that customers can use to instantiate a computing platform and one or more applications, without the complexity of building and maintaining the infrastructure typically associated with these things. Another category is Software as a Service (SaaS) where software is centrally hosted and allocated on a subscription basis. SaaS is also known as on-demand software, web-based software, or web-hosted software. Four technological sub-fields involved in cloud services are: deployment, integration, on demand, and virtual private networks.

The illustrative embodiments recognize and take into account that due to the nature of manual migration of data, it is difficult to get, motivate, and retain technical practitioners with skills, knowledge, and experience on number of data transformation tools for the duration of migration projects.

The illustrative embodiments also recognize and take into account that manual effort in operations associated with data transformation maps poses various challenges that can impact the efficiency and reliability of the operations. In this case, manual cleaning, formatting, and processing data can lead to increased costs and potential delays. Further, the illustrative embodiments also recognize and take into account that manual processes are prone to human errors that can compromise the quality of transformed data.

Thus, illustrative embodiments provide a computer implemented method, apparatus, system, and computer program product for managing different data transformation tools. This management includes migration, modification, and creation of data transformation maps in data transformation tools for different software applications. In one illustrative example, a processor set selects a foundation model to process respective user manuals for a number of data transformation tools. The processor set generates a large language model from the foundation model based on user manuals for the number of data transformation tools and a number of scripts for automation bots for the number of data transformation tools. The processor set generates a number of new automation bots for each data transformation tool in the number of data transformation tools using the large language model. The processor set performs a number of operations associated with data transformation maps in the number of data transformation tools using the number of new automation bots.

As used in herein, a “number of” when used with reference to items means one or more items. For example, a number of processor units is one or more processors.

With reference now to, an illustration of a block diagram of a data transformation environment is depicted in accordance with an illustrative embodiment. In this illustrative example, data transformation environmentincludes components that can be implemented in hardware such as the hardware shown in computing environmentin.

In this illustrative example, data transformation systemin data transformation environmentmanages data transformation tools that can be used for data manipulations. In this illustrative example, data transformation systemincludes computer systemand data transformation assistant. Data transformation assistantis located in computer system. Data transformation assistantmay be implemented using data transformation assistantin.